In a large enterprise, information tends to be distributed among a wide range of heterogeneous data systems. A given database system typically provides a system-specific querying interface, e.g., a database query language allowing users to formulate querying requests in a system-specific format. A query language typically is closely related to aspects of the underlying data. Accordingly, query languages can vary greatly as to syntax and/or power of expressiveness. Users who submit queries to database engines typically must use more than one query language and/or interface to retrieve and integrate related pieces of information together from different data sources. Normally, an underlying database schema not only details a structure of how data is to be organized, but it also provides guidelines about how data is to be queried.
Even though information may be presented in various systems in various ways, different pieces of information can be semantically correlated. Semantics, however, cannot be explicitly represented by many current data models. Semantics may be represented only implicitly (if at all), for example, by internal mechanisms such as database integrity constraints. Traditional query languages built on top of such data models thus are not capable of expressing explicit semantics. For example, when a user uses a relational query language, (s)he frequently has to define one or more join operations in order to inquire as to connections between data items in a plurality of tables. In order to have such join operations exactly reflect the semantics of a query, the user typically needs substantial knowledge of both relational database theory and the query language itself.
An average user typically has little, if any, training in traditional query languages and database systems. A query language thus is desirable which enables a user to use his or her semantic understanding to retrieve information. Additionally, information consumers today include not only human beings, but also computer systems. Thus it also is desirable for the semantics of query answers to be both human- and machine-understandable.